Functional and ecological diversification of underground organs in Solanum
Bibliographic record
Abstract
The evolution of geophytes in response to different environmental stressors is poorly understood largely due to the great morphological variation in underground plant organs, which includes species with rhizomatous structures or underground storage organs (USOs). Here we compare the evolution and ecological niche patterns of different geophytic organs in Solanum L., classified based on a functional definition and using a clade-based approach with an expert-verified specimen occurrence dataset. Results from PERMANOVA and Phylogenetic ANOVAs indicate that geophytic species occupy drier areas, with rhizomatous species found in the hottest areas whereas species with USOs are restricted to cooler areas in the montane tropics. In addition, rhizomatous species appear to be adapted to fire-driven disturbance, in contrast to species with USOs that appear to be adapted to prolonged climatic disturbance such as unfavorable growing conditions due to drought and cold. We also show that the evolution of rhizome-like structures leads to changes in the relationship between range size and niche breadth. Ancestral state reconstruction shows that in Solanum rhizomatous species are evolutionarily more labile compared to species with USOs. Our results suggest that underground organs enable plants to shift their niches towards distinct extreme environmental conditions and have different evolutionary constraints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".